Approaches for local calibration of mechanistic-empirical pavement design guide joint faulting model: a case study of Ontario. Issue Volume 21:Issued 11(2020) (18th September 2020)

• Record Type: Journal Article
• Authors: Dong, Shi; Zhong, Jian; Tighe, Susan L.; Hao, Peiwen; Pickel, Daniel
• Is Part Of: International journal of pavement engineering. Volume 21:Issued 11(2020)
• Page Start: 1347
• Page End: 1361
• Publication Date: 2020-09-18
• Subjects: Mechanistic-empirical pavement design guide -- local calibration -- optimization approach -- jointed plain concrete pavement -- joint faulting; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2018.1545093
• Languages: English
• ISSNs: 1029-8436

Abstract

ABSTRACT: The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed by agencies as an innovative method for pavement design since the National Cooperative Highway Research Program (NCHRP) Project 1-37A was implemented in 2004. Over the years, the MEPDG has evolved into the AASHTOWare Pavement ME Design software (AASHTOWare®). Local calibration of the performance models in the AASHTOWare® is a crucial and challenging task to improve the effectiveness of its application. The accuracy of the calibration depends on efficient methods and validation processes. This paper aims at developing local calibration methods for joint faulting prediction model of Jointed Plain Concrete Pavement (JPCP). This study not only focuses on improving the prediction accuracy of the joint faulting model but also demonstrating the various optimisation procedures in detail. A total of 27 representative JPCP sections were used in the processes of calibration. Three optimisation approaches were used: (1) One-At-a-Time (OAT) through the trial-and-error procedure, (2) generalised reduced gradient (GRG) using MS Excel® Solver, and (3) Levenberg-Marquardt Algorithm (LMA) fitting the functions. The prediction accuracy of local models was improved as compared with the global ones. Average Bias (AB) reduced from 0.3083 to 0.0578, and Standard Error of the Estimate (SEE) reduced from 0.3345 to 0.1912. Among the three local calibration approaches, approach 2 and approach 3 had more significant improvement on results than approach 1. Finally, the integral procedures were provided for local calibration in Ontario.